Anomaly detection based on information technology environment topology

ABSTRACT

Techniques are described for analyzing data regarding activity in an IT environment to determine information regarding the entities associated with the activity and using the information to detect anomalous activity that may be indicative of malicious activity. In an embodiment, a plurality of events reflecting activity by a plurality of entities in an IT environment are processed to resolve the identities of the entities, discover how the entities fit within a topology of the IT environment, and determine what the entities are. This information is then used to generate an entity relationship graph that includes nodes representing the entities in the IT environment and edges connecting the nodes representing interaction relationships between the entities. In some embodiments, baselines are established by monitoring the activity between entities. This baseline information can be represented in the entity relationship graph in the form of directionality applied to the edges. The entity relationship graph can then be monitored to detect anomalous activity.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/420,039 filed on Jan. 30, 2017, and titled “FINGERPRINTING ENTITIESBASED ON ACTIVITY IN AN INFORMATION TECHNOLOGY ENVIRONMENT”, which isincorporated by reference herein in its entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

TECHNICAL FIELD

At least one embodiment of the present disclosure pertains todistributed data processing systems, and more particularly, tointelligence generation and activity discovery from events in adistributed data processing system.

BACKGROUND

Activity detection, both friendly and malicious, has long been apriority for computer network administrators. In known public andprivate computer networks, users employ devices such as desktopcomputers, laptop computers, tablets, smartphones, browsers, etc. tointeract with others through computers and servers that are coupled tothe network. Digital data, typically in the form of data packets, arepassed along the network by interconnected network devices.

Unfortunately, however, malicious activities can cause harm to thenetwork's software or hardware, or its users. Malicious activities mayinclude unauthorized access or subsequent unpermitted use of networkresources and data. Network administrators seek to detect suchactivities, for example, by searching for patterns of behavior that areabnormal or otherwise vary from the expected use pattern of a particularentity, such as an organization or subset thereof, individual user, IPaddress, node or group of nodes in the network, etc.

Security appliances are used in known systems to provide networksecurity. The appliance approach involves installing security appliances(which are typically servers or computers configured for providingsecurity) at one or more locations in the network. Once installed, theappliance monitors traffic that traverses the network. Functionsprovided by the appliance may include malware detection, intrusiondetection, unauthorized access or unauthorized use of data, amongothers. Unfortunately, security appliances cannot easily be scaled tohandle temporary or permanent increases in network traffic. Increasednetwork traffic often requires a security vendor to perform an applianceswap or an equally time-consuming appliance upgrade. Appliances alsotend to have only limited network visibility because they are typicallyconfigured to monitor data traversing the link on which a respectiveappliance is installed only. Such an appliance will be unaware ofactivities occurring on other network segments monitored by otherappliances and thus cannot use the additional context informationpertaining to activities occurring on other network segments to detect acleverly-designed piece of malware that may be difficult to detect frompurely localized information.

Installed software products, rather than security hardware appliances,provide another approach to security for data networks. These products,such as anti-virus or anti-malware software, typically are installed onterminal devices (e.g., desktop and laptop computers, tablets, or smartphones). Data traversing the network between the terminal device ismonitored by the installed products to detect malware in either inboundor outbound data. Unfortunately, installed software products also do notperform well in terms of scalability or network visibility. Installedproducts tend to be disposed locally on the terminal devices and thusalso tend to have fairly localized views of the data on the network.They also tend to be installed on hardware that cannot be upgradedeasily.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIG. 7 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 8 illustrates a block diagram of an example cloud-based data intakeand query system in which an embodiment may be implemented;

FIG. 9 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIG. 10 shows an example of functional layers of a network securitysystem;

FIG. 11 shows a view of processing of data within an example networksecurity system;

FIG. 12 shows the architecture of an example network security platform.

FIG. 13 shows an example implementation of a real-time processing pathin an example network security system;

FIG. 14 shows an example implementation of the data intake andpreparation stage of the network security platform

FIG. 15A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 15B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 16 shows high-level example of a hardware architecture of aprocessing system that can be used to implement the disclosedtechniques;

FIG. 17 shows a high level representation of interconnected entitiescommunicating in an example IT environment;

FIG. 18 shows a flow chart that describes at an example process forfingerprinting entities based on their activities in an IT environment;

FIG. 19 shows a flow diagram that illustrates how raw machine datarelated to activity in an IT environment can be processed to detectanomalies;

FIG. 20 shows an example workflow for entity identity resolution;

FIG. 21 shows another example workflow for entity identity resolution;

FIG. 22 shows yet another example workflow for entity identityresolution;

FIG. 23 shows an example behavioral profile in the form of a vector foran identified entity in an IT environment;

FIG. 24 shows an example event including raw machine data reflectingactivity in an IT environment;

FIG. 25 shows an example event-specific relationship graph based on theevent shown in FIG. 24;

FIG. 26 shows an example representation of an entity relationship graphincluding a plurality of nodes and edges connecting the plurality ofnodes; and

FIG. 27 shows an example representation of the entity relationship graphof FIG. 26 with directionality applied to the edges connecting thenodes.

DETAILED DESCRIPTION

Embodiments are described herein according to the following outline:

-   -   1.0. GENERAL OVERVIEW        -   1.1. ENTITY FINGERPRINTING OVERVIEW    -   2.0. OPERATING ENVIRONMENT        -   2.1. HOST DEVICES        -   2.2. CLIENT DEVICES        -   2.3. CLIENT DEVICE APPLICATIONS        -   2.4. DATA SERVER SYSTEM        -   2.5. DATA INGESTION            -   2.5.1. INPUT            -   2.5.2. PARSING            -   2.5.3. INDEXING        -   2.6. QUERY PROCESSING        -   2.7. FIELD EXTRACTION        -   2.8. EXAMPLE SEARCH SCREEN        -   2.9. DATA MODELS        -   2.10. ACCELERATION TECHNIQUES            -   2.10.1. AGGREGATION TECHNIQUE            -   2.10.2. KEYWORD INDEX            -   2.10.3. HIGH PERFORMANCE ANALYTICS STORE            -   2.10.4. ACCELERATING REPORT GENERATION        -   2.11. CLOUD-BASED SYSTEM OVERVIEW        -   2.12. SEARCHING EXTERNALLY ARCHIVED DATA            -   2.12.1. ERP PROCESS FEATURES        -   2.13. SECURITY FEATURES            -   2.13.1 SECURITY SYSTEM            -   2.13.2 ENTERPRISE SECURITY APPLICATION            -   2.13.3. IT SERVICE MONITORING        -   2.14. COMPUTER PROCESSING SYSTEMS    -   3.0 ENTITY FINGERPRINTING        -   3.1 IDENTITY RESOLUTION        -   3.2 TOPOLOGY DISCOVERY        -   3.3 BEHAVIORAL PROFILING        -   3.4 CLIENT/SERVER RELATIONSHIP DISCOVERY        -   3.5 MONITORING AN ENTITY RELATIONSHIP GRAPH

1.0. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. These components may also be referred toherein as entities. In another example, mobile devices may generatelarge amounts of information relating to data accesses, applicationperformance, operating system performance, network performance, etc.There can be millions of mobile devices that report these types ofinformation.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5).

1.1. Entity Fingerprinting Overview

Modern information technology (IT) environments can include hundreds andperhaps thousands of entities (e.g., devices, users, applications, data,etc.) communicating with each other. This can present a number ofchallenges related to asset inventory and management which can directlyimpact network security. For example, many larger organizations face theissue of shadow IT. Shadow IT refers to situations where informationtechnology systems and solutions are built and used inside organizationswithout explicit organizational approval. The larger and more complexthe IT environment (e.g., more devices and users) the greater thechallenge of shadow IT.

Consider, for example, a IT environment for a large-scale healthcareorganization such as a hospital. Increasingly, assets such as biomedicaldevices are configured to connect to the local networks in suchenvironments, and by extension to other entities outside of the managedlocal network. In many cases, the users of such devices connect thedevices without organizational approval or actively try to skirt certaincontrols to make their lives easier. This can, in part, lead to theissue of shadow IT. The hospital's network now has a number of devicesconnected that may or may not conform with organization standards. Asmentioned, this not only affects inventory and management of assets, butin some cases can expose the organization network to external attacks.

Introduced herein are techniques for addressing these challenges thatinclude, among other things, analyzing data (e.g. events) reflectingactivity in an IT environment to identify entities associated with theactivity, determining information about the entities, and discoveringhow the entities are arranged within a logical structure (i.e. topology)of the IT environment in which they are operating. Once entities areprofiled and the topology of the IT environment is discovered, thisinformation can be modeled and used to detect anomalous communicationsactivity within the IT environment.

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. Pat. No. 9,838,292, entitled“UTILIZING PACKET HEADERS TO MONITOR NETWORK TRAFFIC IN ASSOCIATION WITHA CLIENT DEVICE”, which is hereby incorporated by reference in itsentirety for all purposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally, or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally, or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. Pat. No. 9,130,971, entitled “SITE-BASEDSEARCH AFFINITY,” and in U.S. Pat. No. 9,124,612, entitled “MULTI-SITECLUSTERING”, each of which is hereby incorporated by reference in itsentirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in Fig.) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “I” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in Fig.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. Pat. No.9,129,980, entitled “GENERATION OF A DATA MODEL APPLIED TO QUERIES”,issued on 8 Sep. 2015, and U.S. Pat. No. 9,589,012, entitled “GENERATIONOF A DATA MODEL APPLIED TO OBJECT QUERIES”, issued on 7 Mar. 2017, eachof which is hereby incorporated by reference in its entirety for allpurposes. See, also, Knowledge Manager Manual, Build a Data Model,Splunk Enterprise 6.1.3 pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

2.10. Acceleration Techniques

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high-performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 7 illustrates how a search query 702received from a client at a search head 210 can split into two phases,including: (1) subtasks 704 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 706 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 702, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 702 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 704, and then distributes searchquery 704 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4, or may alternativelydistribute a modified version (e.g., a more restricted version) of thesearch query to the search peers. In this example, the indexers areresponsible for producing the results and sending them to the searchhead. After the indexers return the results to the search head, thesearch head aggregates the received results 706 to form a single searchresult set. By executing the query in this manner, the systemeffectively distributes the computational operations across the indexerswhile minimizing data transfers.

2.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high-performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. Pat. No.9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCE ANALYTICS STOREWITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO AN EVENT QUERY”,issued on 8 Sep. 2015, and U.S. Pat. No. 9,990,386, entitled “STORAGEMEDIUM AND CONTROL DEVICE”, issued on 5 Jun. 2018, each of which ishereby incorporated by reference in its entirety.

2.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criterion, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.11. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 8 illustrates a block diagram of an example cloud-based data intakeand query system. Similar to the system of FIG. 2, the networkedcomputer system 800 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system800, one or more forwarders 204 and client devices 802 are coupled to acloud-based data intake and query system 806 via one or more networks804. Network 804 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 802 and forwarders204 to access the system 806. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 806 forfurther processing.

In an embodiment, a cloud-based data intake and query system 806 maycomprise a plurality of system instances 808. In general, each systeminstance 808 may include one or more computing resources managed by aprovider of the cloud-based system 806 made available to a particularsubscriber. The computing resources comprising a system instance 808may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 802 to access a web portal or otherinterface that enables the subscriber to configure an instance 808.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 808) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment such as SPLUNK® ENTERPRISE and a cloud-basedenvironment such as SPLUNK CLOUD® are centrally visible).

2.12. Searching Externally Archived Data

FIG. 9 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 904 over network connections920. As discussed above, the data intake and query system 108 may residein an enterprise location, in the cloud, etc. FIG. 9 illustrates thatmultiple client devices 904 a, 904 b, . . . , 904 n may communicate withthe data intake and query system 108. The client devices 904 maycommunicate with the data intake and query system using a variety ofconnections. For example, one client device in FIG. 9 is illustrated ascommunicating over an Internet (Web) protocol, another client device isillustrated as communicating via a command line interface, and anotherclient device is illustrated as communicating via a system developer kit(SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 904 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 910. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 910, 912. FIG. 9 shows two ERP processes 910, 912 that connectto respective remote (external) virtual indices, which are indicated asa Hadoop or another system 914 (e.g., Amazon™ S3, Amazon™ EMR, otherHadoop Compatible File Systems (HCFS), etc.) and a relational databasemanagement system (RDBMS) 916. Other virtual indices may include otherfile organizations and protocols, such as Structured Query Language(SQL) and the like. The ellipses between the ERP processes 910, 912indicate optional additional ERP processes of the data intake and querysystem 108. An ERP process may be a computer process that is initiatedor spawned by the search head 210 and is executed by the search dataintake and query system 108. Alternatively, or additionally, an ERPprocess may be a process spawned by the search head 210 on the same ordifferent host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 910, 912 receive a search request from the search head210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 910, 912 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 910, 912 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 910, 912 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices914, 916, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the results,or a processed set of results, based on the returned results to therespective client device.

Client devices 904 may communicate with the data intake and query system108 through a network interface 920, e.g., one or more LANs, WANs,cellular networks, intranetworks, and/or internetworks using any ofwired, wireless, terrestrial microwave, satellite links, etc., and mayinclude the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. Pat. No. 10,049,160, entitled “PROCESSING A SYSTEM SEARCHREQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, issued on 14 Aug.2018, and U.S. Pat. No. 9,514,189, entitled “PROCESSING A SYSTEM SEARCHREQUEST INCLUDING EXTERNAL DATA SOURCES”, issued on 6 Dec. 2016, each ofwhich is hereby incorporated by reference in its entirety for allpurposes.

2.12.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically, the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application. Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.13. Security Features

Various types of networks and enterprises can be susceptible to attacks,in some cases, by users with trusted access. Such attacks often goundetected by existing security products and systems. Indeed,traditional security products often suffer from various limitations,including the inability to detect unknown threats and insider threats,as well as the inability to scale or process huge amount of data.Whether access is obtained by using compromised accounts/systems or byleveraging existing privileges to conduct malicious activities,attackers often do not need to employ additional malware which mightotherwise present an easily detected presence in the target system ornetwork. Accordingly, attacks which rely on the use of seemingly validaccess are difficult to detect, identify, and correct or remediate in atimely manner.

Machine-learning based network security systems can perform userbehavioral analytics (UBA), or more generally user/entity behavioralanalytics (UEBA), to detect security related anomalies and threats,regardless of whether such anomalies and threats are previously known orunknown. Such network security systems can be deployed at any of variouslocations in an information technology environment. In the case of aprivate network (e.g., a corporate intranet), at least parts of thevarious described systems can be implemented at a strategic location(e.g., a router or a gateway coupled to an administrator's computerconsole) that can monitor or control the network traffic within theprivate intranet. In the case of cloud-based application where anorganization may rely on Internet-based computer servers for datastorage and data processing, at least parts of the various describedsystems can be implemented at, for example, the cloud-based servers.Additionally, or alternatively, the various described systems can beimplemented in a private network but nonetheless receive/monitor eventsthat occur on the cloud-based servers. In some embodiments, the variousdescribed systems can monitor a hybrid of both intranet and cloud-basednetwork traffic.

In this description, an “anomaly” is defined as a detected or identifiedvariation from an expected pattern of activity on the part of an entityassociated with an information technology environment, which may or maynot constitute a threat. This entity activity that departs from expectedpatterns of activity can be referred to as “anomalous activity.” Forexample, an anomaly may include an event or set of events of possibleconcern that may be actionable or warrant further investigation.Examples of anomalies include alarms, blacklistedapplications/domains/IP addresses, domain name anomalies, excessiveuploads or downloads, website attacks, land speed violations, machinegenerated beacons, login errors, multiple outgoing connections, unusualactivity time/sequence/file access/network activity, etc.

An anomaly or a set of anomalies can be evaluated and may result in adetermination of a threat indicator or a threat. A threat is aninterpretation of one or more anomalies or threat indicators. Threatindicators and threats are escalations of events of concern. Examples ofthreats include data exfiltration (e.g., by compromised account, bymalware, or by a suspicious user or device), public-facing websiteattack, suspicious behavior by an insider, and breach of a rule (e.g.access by a blacklisted user or an unauthorized file transfer). Like ananomaly, a threat can be associated with one or more entities, includingusers, devices, and applications.

2.13.1 Security System

FIG. 10 illustrates a high-level view of an architecture 1000 of anexample network security system 120 that may be part of the networkedcomputer system 100 described with respect to FIG. 1. Note that in FIG.1, the network security system 120 is show as separate from data intakeand query system 108 for illustrative purposes, however it shall beappreciated that their respective functionalities may be combined intofewer or more systems than as shown. In some embodiments systems 108 and120 may be implemented at a single computing device or distributedacross multiple network-connected computing devices. Similarly, therespective functionalities of systems 108 and 120 may be implemented asone or more services by one or more service providers. These servicesmay be accessible to end-users via any of client applications 110 orhost applications 114. For example, in some embodiments, servicesassociated with systems 108 and 120 may be offered by a single serviceprovider as part of an integrated network security platform.

In FIG. 10, a cloud computing infrastructure is shown, represented inpart by a virtualization layer 1004. Various cloud computing operatingsystems or platforms, such as OpenStack™, VMware™, Amazon Web Services™,Google Cloud™, or the like, may be employed in virtualization layer 1004to create public clouds or private clouds. Generally speaking, thesecloud computing operating systems and others permit processing andstorage to be implemented on top of a set of shared resources. Among itsmany advantages, cloud computing permits or facilitates redundancy,fault tolerance, easy scalability, low implementation cost and freedomfrom geographic restrictions.

Above the virtualization layer 1004, a software framework layer 1006implements the software services executing on the virtualization layer1004. Examples of such software services include open-source softwaresuch as Apache Hadoop™, Apache Spark™, and Apache Storm™ Apache Hadoop™is an example of an open-source software framework for distributedstorage and distributed processing of very large data sets on computerclusters built from commodity hardware. Apache Storm™ is an example of adistributed real-time computation engine that processes data streamrecord-by-record. Apache Spark™ is an example of a large-scale dataprocessing engine that collects events together for processing inbatches. These are only examples of software that may be employed inimplementations of the software framework layer 606.

A security intelligence layer 1022 implements a security semantic layer1008, a machine learning layer 1010, and a rules layer 1012. Thesecurity semantic layer 1008 can perform the extract, transform, andload (ETL) functions that prepare the incoming event data for furtherprocessing by downstream consumers. Note that the term ETL here is usedin an illustrative sense to facilitate understanding, as the ETL stagedescribed herein may include functionality in addition to or differentfrom traditional ETL techniques. The machine learning layer 1010represents one of the consumers of the data output of the securitysemantic layer 1008. The rules layer 1012 represents another possibleconsumer of data output from the security semantic layer 608. In anexample, event data may be received by the security semantic layer 1008,and prepared (or “pre-processed”) to be further processed by the machinelearning layer 1010 or rules layer 1012.

Above the security intelligence layer 1022 is an application layer 1014.The application layer 1014 represents the layer in which applicationsoftware modules may be implemented. For example, applications part ofapplication layer 1014 may be implemented as one or more clientapplications 110 or host applications 114 described with respect toFIG. 1. In an example, the output of the rules layer 1012 includeanomalies and the output of the machine learning layer 1010 includesanomalies, threat indicators, or threats. This output may be analyzed bythe various applications such as a threat detection application 1016, asecurity analytics application 1018 or other applications 1020. Also, insome embodiments users may provide input via applications in theapplications layer to specify rules in the rules layer 1012 or modifymachine learning algorithms in the machine learning layer 1010. Anexample of an applications that may be implemented in the applicationslayer 1014 is the Splunk® App for Enterprise Security (described below).These layers, modules and their operation will be discussed in greaterdetail below

FIG. 11 shows a conceptual view of the processing of data within networksecurity system 120, according to some embodiments. A receive data block1102 represents a logical component in which event data and other dataare received from one or more data sources. In an example, receive datablock 1102 includes application programming interfaces (APIs) forcommunicating with various data sources. An ETL block 1104 is the datapreparation component in which data received from the receive data block1102 is pre-processed, for example, by adding data or metadata to theevent data (a process interchangeably called decoration, enrichment orannotation herein), or otherwise prepared, to allow more effectiveconsumption by downstream data consumers (e.g., machine learningmodels).

The enriched event data from the ETL block 1104 is then provided to areal-time analyzer 1110 over a real-time processing path 1112 fordetecting anomalies, threat indicators and threats. Output 1114 from thereal-time analyzer 1110 is provided for action by the human operator, incertain embodiments. It should be noted that the real-time analyzer 1110operates in real-time by analyzing event data as the event data receivedby the security system 120.

The event data from the ETL block 1104 is also provided to a batchanalyzer 1140 over a batch processing path 1142 for detecting anomalies,threat indicators and threats. However, while the event data is providedto the real-time analyzer 1110 in an unbounded, streaming,record-by-record manner, it is provided to the batch analyzer in theform of batches of event data (i.e., where each batch of event datacontains a collection of events that arrived over the batch period).Because the batch analyzer 1140 processes data in batch mode instead ofin real-time, in addition to the event data that the real-time analyzer1110 receives, the batch analyzer 1140 can receive additional historicalevent data from the security platforms, prior analysis (including theanalysis results, the model states, and the supporting data) from thereal-time analyzer 1110 (e.g., through a model management component1160), or prior analysis from other analyzers (real-time or batch)implemented elsewhere in the same or other networks.

Machine learning models are employed to evaluate and analyze data incertain embodiments, that is not necessarily the case in everyembodiment. In some cases, the security platform may also adapt moreappropriately or more efficiently to the environment by using acombination of other suitable forms of analysis, including rule-basedanalysis, algorithm-based analysis, statistical analysis, etc. Forexample, as previously described, in some embodiments, anomaliesdetected using rule-based analysis can be input and combined withanomalies detected using the real time analyzer 1110 or batch analyzer1140 to detect threat indicators or threats.

FIG. 12 illustrates an example of an overall architecture 1200 of anetwork security system 120. Data sources 1202 represent various datasources that provide data including events (e.g. machine data) and otherdata, to be analyzed for anomalies and threats. The incoming data caninclude event data represents events that take place in the networkenvironment. For example, data source 1204 is a source of datapertaining to logs including, for example, user log-ins and other accessevents. These records may be generated from operational (e.g., networkrouters) and security systems (e.g., firewalls or security softwareproducts). Data source 1206 is a source of data from different types ofapplications, including software as a service (e.g., Box™). Data source1206 may use different mechanisms for transmitting the event data,including a push mechanism, a pull mechanism, or a hybrid mechanism.Other data sources which may fall into the data source 1206 categoryinclude human resource systems, accounting systems, customer relationdatabases, and the like. Data source 1208 is a source of networkmanagement or analyzer data (e.g., event data related to traffic on anode, a link, a set of nodes, or a set of links). The network managementor analyzer data may be obtained from various network operating systemsand protocols, such as Cisco Netflow™. The data sources mentioned hereare only examples, as other suitable data sources may also be used.

The data sources 1202 provide data to data receivers 1210, whichimplement various APIs and connectors to receive (or retrieve, dependingon the mechanism) the data for the security system 120. The datareceivers 1210 may also optionally filter some of the data. For example,to reduce the workload of the security system, a business rule may beset to state that all query events to “www.google.com” should befiltered out as not interesting (e.g., this type of access is determinednot to represent any security threat). Technologies employed toimplement the data receiver 1210 may include Flume™ and REST™. Flume™ isan open-source distributed service for collecting, aggregating, andmoving large amounts of log data. REST™ is an interface for accessinglarge databases.

The received data can then be provided via a channel 1214 to a semanticprocessor (or data preparation stage) 1216, which in certain embodimentsperforms, among other functions, ETL functions. In particular, thesemantic processor 1216 may perform parsing of the incoming event data,enrichment (also called decoration or annotation) of the event data withcertain information, and optionally, filtering the event data. Thesemantic processor 1216 introduced here is particularly useful when datareceived from the various data sources through data receiver 1210 is indifferent formats, in which case the semantic processor 1216 can preparethe data for more efficient downstream utilization (including, forexample, by an event processing engine) while avoiding binding theunstructured data into any particular type of data structure.

A parser in the semantic processor 1216 may parse the various fields ofreceived event data representing an event (e.g., a record related to alog-in event). An identity resolution (IR) component (not shown in FIG.12) may be optionally provided within the semantic processor 1216 tocorrelate IP addresses with users, for example. This correlation permitsthe security system 120 to make certain assumptions about therelationship between an IP address and a user so that, if any event dataarrives from that IP address in the future, an assumption regardingwhich user is associated with that IP address may be made. In someimplementations, the event data pertaining to that IP address may beannotated with the identity of the user. Technology used to implementthe data preparation functions of the semantic processor 1216 mayinclude Redis™.

An optional filter attribution block 1222 in the semantic processor 1216removes certain pre-defined events. The attribution filter 1222 in thesemantic processor 1216 may further remove events that need not beprocessed by the security system 120. An example of such an event is aninternal data transfer that occurs between two IP addresses as part of aregular file backup. In some embodiments, the functions of semanticprocessor 1216 are configurable by a configuration file to permit easyupdating or adjusting. Examples of configurable properties of thesemantic processor 1216 include how to (i) parse events, (ii) correlatebetween users and IP address, or (iii) correlate between one attributewith another attribute in the event data or an external attribute. Theconfiguration file can also adjust filter parameters and otherparameters in the semantic processor 1216.

Data processed by the semantic processor 1216 is sent to a distributionblock 1220. The distribution block 1220 can be a messaging mechanism todistribute data to one or both of the real-time processing path and thebatch processing path. The real-time processing path is entered via theright-facing arrow extending from the distribution block 1220, whereasthe batch processing path is entered via arrow 1288 extending downwardfrom the distribution block 1220.

The real-time processing path includes an analysis module 1230 thatreceives data from the distribution block 1220. The analysis module 1230analyzes the data in real-time to detect anomalies, threat indicators,and threats. In certain embodiments, the aforementioned Storm™ platformmay be employed to implement the analysis module 1230. In otherembodiments, the analysis module could be implemented by using ApacheSpark Streaming.

These anomalies, threat indicators and threats may be provided to a userinterface (UI) system 1250 for review by a human operator 1252. Userinterface 1250 may be provided via nay number of applications or othersystems. For example, in an embodiment, anomaly, threat, and threatindicator data is output for display via a UI at an enterprise securityapplication (e.g. Splunk® App for Enterprise Security—described below).As an example, a visualization map and a threat alert may be presentedto the human operator 1252 for review and possible action. The output ofthe analysis module 1230 may also automatically trigger actions such asterminating access by a user, terminating file transfer, or any otheraction that may neutralize the detected threats. In certain embodiments,only notification is provided from the analysis module 1230 to the UIsystem 1250 for review by the human operator 1252. The event data thatunderlies those notifications or that gives rise to the detection madeby the analysis module 1230 are persistently stored in a database 1278.If the human operator decides to investigate a particular notification,he or she may access from database 1278 the event data (including rawevent data and any associated information) that supports the anomaliesor threat detection. On the other hand, if the threat detection is afalse positive, the human operator 1252 may so indicate upon beingpresented with the anomaly or the threat. The rejection of the analysisresult may also be provided to the database 1278. The operator feedbackinformation (e.g., whether an alarm is accurate or false) may beemployed to update the model to improve future evaluation.

Arrow 1260 represents the storing of data supporting the analysis of theanomalies and threats in the real-time path. For example, the anomaliesand threats as well as the event data that gives rise to detection ofthe anomalies and threats may be stored in database 1278 (e.g., an SQLstore) using a path represented by the arrow 1260. Additionalinformation such as the version of the models, the identification of themodels used, and the time that the detection is made, may also bestored.

The human operator 1252 may review additional information in response tothe notification presented by the UI system 1250. The data supportingthe analysis of the anomalies and threats may be retrieved from database1278 via an access layer 1264. Arrow 1262 represents a data retrievalrequest via the access layer 1264 to one or more of databases 1270,1272, 1274 and 1278. In some embodiments, event data associated withstored anomalies, threats, and threat indicators may be retrieved from adata intake and security query system 108 in response to a querytransmitted to search head 210. The data served up by the databases canbe provided to the UI 1250 by means of data pathway 1280. The accesslayer 1264 includes the APIs for accessing the various databases and theuser interfaces in the UI 1250. For example, block 1266A represents theAPI for accessing the HBase or HDFS (Hadoop File Service) databases.Block 1266B represents the various APIs compatible for accessing serversimplementing sockets.io or node.js servers. SQL API 1266C represents theAPI for accessing the SQL data store 1278, which stores data pertainingto the detected threats and anomalies.

Line 1268 is a conceptual line that separates the batch processing path(below line 868) from the real-time processing path (above line 1268).The infrastructure which may operate in batch mode includes the SQLstore 1278 that stores information accessible by scripted query language(SQL), a time series database 1270 that represents the database forstoring time stamped data, an HBase 1272 that can be an open-source,distributed, non-relational database system on which databases (e.g.,the time serious database 1270) can be implemented, and a GraphDBdatabase 1274 that stores security graphs 1292, which may be based onrelationship graphs generated from events. In some embodiments, theGraphDB database 1274 comprises a Neo4j™ graph database.

A security graph, as described further below, is generally arepresentation of the relationships between entities in the network andany anomalies identified. For example, a security graph may map out theinteractions between users, including information regarding whichdevices are involved, who or what is talking to whom/what, when and howinteractions occur, which nodes or entities may be anomalous, and thelike. The nodes of the security graph may be annotated with additionaldata if desired.

A batch analysis module 1282 is the analysis module that processes datain batches. The analysis module 882 may take into account the historicalevent data stored in databases 1270, 1272, 1274, and 1278 (including“relatively” contemporary event data that is passed from distributionblock 1220 to the persistent layer below line 1268 via network channel1288). In one example, the batch analysis module 1282 may employ thirdparty data 1284. With more time allowance and more data available foranalysis, the batch analysis module 1282 may be able to uncoveradditional anomalies and threats that may not be easily detectable bythe real-time analysis module 1230. The model management block 1286includes a model store and a model registry. The model registry canstore model type definitions for machine learning models, and the modelstore can store model states for machine learning models.

In certain embodiments, the models that are employed for evaluation byone analysis module may be shared with another module. Model statesharing 1290 may improve threat detection by various modules (e.g., twomodules belonging to an international network of the same company, butone deployed in Asia and another one deployed in North America; or, onemodule being used in the real-time path and another in the batch path)as the model state sharing leverages knowledge learned from one moduleto benefit others. Security graphs 1292 may also be shared amongmodules, and even across different organizations. For example,activities that give rise to a detection of anomalies or a threat in oneenterprise may thus be shared with other enterprises. Hadoop nodes 1294represent the use of cloud-based big data techniques for implementingthe architecture of FIG. 12 to improve scalability as well as theability to handle a large volume of data. Control path 1296 representsthe control software that may be used for monitoring and maintaining thesecurity system 120.

FIG. 13 shows an example implementation of the real-time processing pathin greater detail. With reference to both FIGS. 12 and 13, the analysismodule 1230 has been expanded as two analysis modules 1230 a and 1230 bto represent the anomaly detection stage and the threat detection stage,respectively. The analysis module 1230 a is responsible for detectinganomalies, and the output of the analysis module 1230 a is provided tothe analysis module 1230 b for detecting threats or threat indicatorsbased on the detected anomalies. In practice, the two stages may beperformed by the same module utilizing different models in a stagedmanner.

The output of analysis module 1230 a, representing anomalies detectedusing machine learning-based models, is provided to an anomaly writer1302. The anomaly writer 1302 can store the anomalies (e.g., includingevent data representing an anomalous event and any associatedinformation) in the database 1278. The same anomalies may also be storedin the time series database 1270 and the HBase 1272. The anomalies mayalso be stored in the graph database 1274. In some embodiments, theanomalies can be stored in graph database 1274 in the form of anomalynodes in a graph or graphs; specifically, after an event is determinedto be anomalous, an event-specific relationship graph associated withthat event can be updated (e.g., by the anomaly writer 1302) to includean additional node that represents the anomaly, as discussed furtherbelow. Certain embodiments of the security system 120 provide theability to aggregate, at a specified frequency (e.g., once a day), theindividual event-specific relationship graphs from all the processedevents in order to compose a composite relationship graph for a givenenterprise or associated network. This aforementioned update to anindividual event's relationship graph allows the composite relationshipgraph to include nodes representing anomalies, thereby providing moresecurity-related information. The individual event-specific relationshipgraph and the composite relationship graph are discussed in more detailbelow. The information stored may include the anomalies themselves andalso relevant information that exists at the time of evaluation. Thesedatabases allow rapid reconstruction of the anomalies and all of theirsupporting data.

The output from the analysis modules 1230 b, representing threats, maybe stored in the database 1278, the times series database 1270 or theHbase 1272. As in the case of anomalies, not only are the threatsthemselves stored, but relevant information that exists at the time ofevaluation can also be stored.

FIG. 14 shows an example implementation of a data intake and preparationstage 1400 of the security system 120. The data intake and preparationstage (or engine) 1400 can be an implementation of ETL stage 1216 inFIG. 12. The data intake and preparation stage 1400 can include a numberof components that perform a variety of functions disclosed herein. Inthe example of stage 1400, the data intake and preparation stage of thesecurity system 120 includes a number of data connectors 1402, a formatdetector 1404, a number of parsers 1406, a field mapper 1408, arelationship graph generator 1410, an identity resolution module 1412, anumber of decorators 1414, and event view adder 1416. These components(e.g., sets of instructions) need not be implemented as separatesoftware programs, procedures or modules, and thus various subsets ofthese components may be combined or otherwise rearranged in variousembodiments. Also, the components shown in FIG. 14 are only one exampleof the data intake and preparation stage components that can be used bythe security system 120; the data intake and preparation stage couldhave more or fewer components than shown, or a different configurationof components.

The various components shown in FIG. 14 can be implemented by usinghardware, software, firmware or a combination thereof, including one ormore signal processing or application specific integrated circuits. Thecomponents in the stage 1400 are shown arranged in a way thatfacilitates the discussion herein; therefore, any perceivable sequencein the stage 1400 is merely an example and can be rearranged. Any stepin the stage 1400 may be performed out-of-sequence or in parallel to theextent that such rearrangement does not violate the logic dependency ofthe steps. One or more steps described for the stage 1400 may beoptional, depending on the deployed environment. The data output fromthe data intake and preparation stage 1400 can also be referred toherein as “decorated events” or “event feature sets.” A decorated eventincludes the raw machine data associated with an event, plus anydecoration, enrichment, information, or any other suitable intelligencethat is generated based upon or extracted from the event during the dataintake and preparation stage. In some embodiments, because of thecomputationally intensive processes that the data intake and preparationstage may perform, the data intake and preparation engine may beimplemented separately from the rest of the stages in the securitysystem 120, for example, on a standalone server or on dedicated nodes ina distributed computer cluster.

Various data connectors 1402 can be employed by the security system 120(e.g., at the data intake stage) to support various data sources.Embodiments of the data connectors 1402 can provide support foraccessing/receiving indexed data, unindexed data (e.g., data directlyfrom a machine at which an event occurs), data from a third-partyprovider (e.g., threat feeds such as Norse™, or messages from AWS™CloudTrail™), or data from a distributed file system (e.g., HDFS™).Hence, the data connectors 1402 enable the security system 120 to obtainevents (e.g. containing machine data) from various different datasources. Some example categories of such data sources include:

(1) Identity/Authentication: e.g., active directory/domain controller,single sign-on (SSO), human resource management system (HRMS), virtualprivate network (VPN), domain name system (DNS), or dynamic hostconfiguration protocol (DHCP);

(2) Activity: e.g., web gateway, proxy server, firewall, Netflow™, dataloss prevention (DLP) server, file server, or file host activity logs;

(3) Security Products: e.g., endpoint security, intrusion preventionsystem, intrusion detection system, or antivirus;

(4) Software as a Service (SaaS) or Mobile: e.g., AWS™ CloudTrail™, SaaSapplications such as Box™ or Dropbox™, or directly from mobile devices;and

(5) External Threat Feeds: e.g., Norce™, TreatStream™, FinancialServices Information Sharing and Analysis Center (FS-ISAC)™, orthird-party blacklisted IP/domains.

Depending on the embodiment, external threat feeds may directly feed tothe security system 120, or indirectly through one or more securityproducts that may be coexisting in the environment within which thesecurity system 120 is deployed. As used herein, the term “heterogeneousevent” refers to the notion that incoming events may have differentcharacteristics, such as different data formats, different levels ofinformation, and so forth. Heterogeneous events can be a result of theevents originating from different machines, different types of machines(e.g., a firewall versus a DHCP server), being in a different dataformat, or a combination thereof.

The data connectors 1402 can implement various techniques to obtainmachine data from the data sources. Depending on the data source, thedata connectors 1402 can adopt a pull mechanism, a push mechanism, or ahybrid mechanism. For those data sources (e.g., a query-based system,such as Splunk®) that use a pull mechanism, the data connectors 1402actively collect the data by issuing suitable instructions to the datasources to grab data from those data sources into the security system120. For those data sources (e.g., ArcSignt™) that use a push mechanism,the data connectors 1402 can identify an input (e.g., a port) for thedata sources to push the data into the system. The data connectors 1402can also interact with a data source (e.g., Box™) that adopts a hybridmechanism. In one embodiment of the data connectors 1402 for such hybridmechanism, the data connectors 1402 can receive from the data source anotification of a new event, acknowledges the notification, and at asuitable time communicate with the data source to receive the event.

For those data connectors 1402 that may issue queries, the queries canbe specifically tailored for real-time (e.g., in terms of seconds orless) performance. For example, some queries limit the amount of theanticipated data by limiting the query to a certain type of data, suchas authentication data or firewall related data, which tends to be morerelevant to security-related issues. Additionally or alternatively, somequeries may place a time constraint on the time at which an event takesplace.

Moreover, in some examples, the data connectors 1402 can obtain datafrom a distributed file system such as HDFS™. Because such a system mayinclude a large amount of data (e.g., terabytes of data or more), it ispreferable to reduce data movement so as to conserve network resources.Therefore, some embodiments of the data connectors 1402 can generate anumber of data processing jobs, send the jobs to a job processingcluster that is coupled to the distributed file system, and receive theresults from the job processing cluster. For example, the dataconnectors 1402 can generate MapReduce™ jobs, and issue those jobs to ajob processing cluster (e.g., YARN™) that is coupled to the distributedfile system. The output of the job processing cluster is received backinto the security system 120 for further analysis, but in that case, noor very little raw machine data is moved across the network. The data isleft in the distributed file system. In some examples, the generatedjobs are user behavior analysis related.

Optionally, after the data connectors 1402 obtain/receive the data, ifthe data format of the data is unknown (e.g., the administrator has notspecified how to parse the data), then the format detector 1404 can beused to detect the data format of the input data. For example, theformat detector 1404 can perform pattern matching for all known formatsto determine the most likely format of a particular event data. In someinstances, the format detector 1404 can embed regular expression rulesor statistical rules in performing the format detection. Some examplesof the format detector 1404 employ a number of heuristics that can use ahierarchical way to perform pattern matching on complex data format,such as an event that may have been generated or processed by multipleintermediate machines. In one example, the format detector 1404 isconfigured to recursively perform data format pattern matching bystripping away a format that has been identified (e.g., by strippingaway a known event header, like a Syslog header) in order to detect aformat within a format.

However, using the format detector 1404 to determine what data formatthe input data may be at run time may be a time- and resource-consumingprocess. At least in the cybersecurity space, it is typical that theformats of the machine data are known in advance (e.g., an administratorwould know what kind of firewall is deployed in the environment).Therefore, as long as the data source and the data format are specified,the data intake and preparation stage can map the data according toknown data formats of a particular data source, without the need ofperforming data format detection. In certain embodiments, the securitysystem 120 can prompt (e.g., through a user interface) the administratorto specify the data format or the type of machine(s) the environmentincludes, and can automatically configure, for example, the parsers 1406in the data intake and preparation stage for such machines.

Further, the security system 120 provides a way to easily supporting newdata format. Some embodiments provide that the administrator can createa new configuration file (e.g., a configuration “snippet”) to customizethe data intake and preparation stage for the environment. For example,for a particular data source, the configuration file can identify, inthe received data representing an event, which field represents a tokenthat may correspond to a timestamp, an entity, an action, an IP address,an event identifier (ID), a process ID, a type of the event, a type ofmachine that generates the event, and so forth. In other examples (e.g.,if a new data format is binary), then the security system 120 allows anadministrator to leverage an existing tokenizer/parser by changing theconfiguration file, or to choose to implement a new, customized parseror tokenizer.

In a number of implementations, through the configuration file (e.g.,snippet), the administrator can also identify, for example, fieldmappings, decorators, parameters for identity resolution (IR), or otherparameters of the data intake and preparation stage. The configurationsnippet can be monitored and executed by the data intake and preparationengine on the fly to allow an administrator to change how variouscomponents in the data intake and preparation engine functions withoutthe need to recompile codes or restart the security system 120.

After receiving data by the data connectors 1402, the parsers 1406 parsethe data according to a predetermined data format. The data format canbe specified in, for example, the configuration file. The data formatcan be used for several functions. The data format can enable the parserto tokenize the data into tokens, which may be keys, values, or morecommonly, key-value pairs. Examples of supported data format includemachine data output as a result of active-directory activity, proxyactivity, authentication activity, firewall activity, web gatewayactivity, virtual private network (VPN) connection activity, anintrusion detection system activity, network traffic analyzer activity,or malware detection activity.

Each parser can implement a set of steps. Depending on what type of datathe data intake and preparation stage is currently processing, in someembodiments, the initial steps can include using regular expression toperform extraction or stripping. For example, if the data is a systemlog (syslog), then a syslog regular expression can be first used tostrip away the packet of syslog (i.e., the outer shell of syslog) toreveal the event message inside. Then, the parser can tokenize the eventdata into a number of tokens for further processing.

The field mapper 1408 can map the extracted tokens to one or morecorresponding fields with predetermined meanings. For example, the dataformat can assist the field mapper 1408 to identify and extract entitiesfrom the tokens, and more specifically, the data format can specifywhich of the extracted tokens represent entities. In other words, thefield mapper 1408 can perform entity extraction in accordance with thoseembodiments that can identify which tokens represent entities. An entitycan include, for example, a user, a device, an application, a session, auniform resource locator (URL), or a threat. Additionally, the dataformat can also specify which tokens represent actions that have takenplace in the event. Although not necessarily, an action can be performedby one entity with respect to another entity; examples of an actioninclude use, visit, connect to, log in, log out, and so forth. In yetanother example, the filed mapper 1108 can map a value extracted to akey to create a key-value pair, based on the predetermined data format.

The entity extraction performed by the field mapper 1404 enables thesecurity system 120 to gain potential insight on the environment inwhich the security system is operating, for example, who the users are,how many users there may be in the system, how many applications thatare actually being used by the users, or how many devices there are inthe environment.

2.13.2 Enterprise Security Application

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. Pat. No. 9,215,240, entitled “INVESTIGATIVE AND DYNAMIC DETECTIONOF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS IN BIG DATA”, issuedon 15 Dec. 2015, U.S. Pat. No. 9,173,801, entitled “GRAPHIC DISPLAY OFSECURITY THREATS BASED ON INDICATIONS OF ACCESS TO NEWLY REGISTEREDDOMAINS”, issued on 3 Nov. 2015, U.S. Pat. No. 9,248,068, entitled“SECURITY THREAT DETECTION OF NEWLY REGISTERED DOMAINS”, issued on 2Feb. 2016, U.S. Pat. No. 9,426,172, entitled “SECURITY THREAT DETECTIONUSING DOMAIN NAME ACCESSES”, issued on 23 Aug. 2016, and U.S. Pat. No.9,432,396, entitled “SECURITY THREAT DETECTION USING DOMAIN NAMEREGISTRATIONS”, issued on 30 Aug. 2016, each of which is herebyincorporated by reference in its entirety for all purposes.Security-related information can also include malware infection data andsystem configuration information, as well as access control information,such as login/logout information and access failure notifications. Thesecurity-related information can originate from various sources within adata center, such as hosts, virtual machines, storage devices andsensors. The security-related information can also originate fromvarious sources in a network, such as routers, switches, email servers,proxy servers, gateways, firewalls and intrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 15A illustrates anexample key indicators view 1500 that comprises a dashboard, which candisplay a value 1501, for various security-related metrics, such asmalware infections 1502. It can also display a change in a metric value1503, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 1500 additionallydisplays a histogram panel 1504 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes. Thesevisualizations can also include an “incident review dashboard” thatenables a user to view and act on “notable events.” These notable eventscan include: (1) a single event of high importance, such as any activityfrom a known web attacker; or (2) multiple events that collectivelywarrant review, such as a large number of authentication failures on ahost followed by a successful authentication. For example, FIG. 15Billustrates an example incident review dashboard 1510 that includes aset of incident attribute fields 1511 that, for example, enables a userto specify a time range field 1512 for the displayed events. It alsoincludes a timeline 1513 that graphically illustrates the number ofincidents that occurred in time intervals over the selected time range.It additionally displays an events list 1514 that enables a user to viewa list of all of the notable events that match the criteria in theincident attributes fields 1511. To facilitate identifying patternsamong the notable events, each notable event can be associated with anurgency value (e.g., low, medium, high, critical), which is indicated inthe incident review dashboard. The urgency value for a detected eventcan be determined based on the severity of the event and the priority ofthe system component associated with the event.

2.13.3. IT Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE™, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE™ system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE™ enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPIs) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE™ application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a search querythat derives a KPI value from the machine data of events associated withthe entities that provide the service. Information in the entitydefinitions may be used to identify the appropriate events at the time aKPI is defined or whenever a KPI value is being determined. The KPIvalues derived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to search query processing. AggregateKPIs may be defined to provide a measure of service performancecalculated from a set of service aspect KPI values; this aggregate mayeven be taken across defined timeframes and/or across multiple services.A particular service may have an aggregate KPI derived fromsubstantially all of the aspect KPI's of the service to indicate anoverall health score for the service.

SPLUNK® IT SERVICE INTELLIGENCE™ facilitates the production ofmeaningful aggregate KPI's through a system of KPI thresholds and statevalues. Different KPI definitions may produce values in differentranges, and so the same value may mean something very different from oneKPI definition to another. To address this, SPLUNK® IT SERVICEINTELLIGENCE™ implements a translation of individual KPI values to acommon domain of “state” values. For example, a KPI range of values maybe 1-100, or 50-275, while values in the state domain may be critical,’warning,’ normal,’ and informational’. Thresholds associated with aparticular KPI definition determine ranges of values for that KPI thatcorrespond to the various state values. In one case, KPI values 95-100may be set to correspond to ‘critical’ in the state domain. KPI valuesfrom disparate KPI's can be processed uniformly once they are translatedinto the common state values using the thresholds. For example, “normal80% of the time” can be applied across various KPI's. To providemeaningful aggregate KPI's, a weighting value can be assigned to eachKPI so that its influence on the calculated aggregate KPI value isincreased or decreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE™ can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE™ can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be created and updated by an import of tabulardata (as represented in a CSV, another delimited file, or a search queryresult set). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in SPLUNK® IT SERVICE INTELLIGENCE™ can also be associatedwith a service by means of a service definition rule. Processing therule results in the matching entity definitions being associated withthe service definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE™ can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (ITSM)system, such as a systems available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE™ provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE™ provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

2.14. Computer Processing Systems

Techniques described in this disclosure can be implemented using one ormore conventional physical processing devices. FIG. 16 is a blockdiagram showing an example of such a processing device, e.g., a computersystem 1600. Multiple instances of such a computer system may be used toimplement any of the aforementioned systems including networked computersystem 100.

In an illustrative embodiment, computer system 1600 includes one or moreprocessor(s) 1610, memory 1620, one or more input/output (I/O) devices1630, a network adapter 1640, and a storage adapter 1650, allinterconnected by an interconnect 1660. Memory 1620 includes storagelocations that are addressable by processor(s) 1610 and adapters 1640and 1650 for storing software program code and data structuresassociated with the techniques introduced here. Memory 1620 may includemultiple physically distinct memory devices, which may be all of thesame type or of different types (e.g., volatile memory such as SRAM orDRAM, non-volatile memory such as flash, etc.). Processor(s) 1610 andadapters 1640 and 1650 may, in turn, include processing elements orlogic circuitry configured to execute the software code and manipulatethe data structures. It will be apparent to those skilled in the artthat other processing and memory implementations, including variousmachine-readable storage media, may be used for storing and executingprogram instructions pertaining to the techniques introduced here.

Network adapter 1640 includes one or more ports to couple computersystem 1600 with one or more other devices over one or morepoint-to-point links, local area networks (LANs), wide area networks(WANs), the global Internet, virtual private networks (VPNs) implementedover a public network, or the like. Network adapter 1640 can include themechanical components and electrical circuitry needed to connect storageserver 1600 to a network. One or more systems can communicate with othersystems over the network by exchanging packets or frames of dataaccording to pre-defined protocols, such as TCP/IP.

Storage adapter 1650 interfaces with an operating system running onprocessor(s) 1610 to access information on attached storage devices. Theinformation may be stored on any type of attached array of writablestorage media, such as hard disk drives, magnetic tape, optical disk,flash memory, solid-state drives, RAM, MEMs or any other similar mediaadapted to store information. Storage adapter 1650 includes a pluralityof ports having I/O interface circuitry that couples with disks or otherstorage related devices over an I/O interconnect arrangement.

Embodiments of the techniques introduced here include various steps andoperations, which have been described above. A variety of these stepsand operations may be performed by hardware components or may beembodied in machine-executable instructions, which may be used to causeone or more general-purpose or special-purpose processors programmedwith the instructions to perform the steps. Alternatively, the steps maybe performed by a combination of hardware, software, or firmware.

Embodiments of the techniques introduced here may be implemented, atleast in part, by a computer program product which may include anon-transitory machine-readable medium having stored thereoninstructions that may be used to program/configure a computer or otherelectronic device to perform some or all of the operations describedabove. The machine-readable medium may include, for example, magnetichard disk drives, compact disc read-only memories (CD-ROMs),magneto-optical disks, floppy disks, ROMs, RAMs, various forms oferasable programmable read-only memories (EPROMs), magnetic or opticalcards, flash memory, or other type of machine-readable medium suitablefor storing electronic instructions. Moreover, embodiments of thepresent invention may also be downloaded as a computer program product,wherein the program may be transferred from a remote computer to arequesting computer by way of data signals embodied in a carrier wave orother propagation medium via a communication link.

3.0 Entity Fingerprinting

As previously discussed, an entity fingerprinting process can beemployed to address issues related to shadow IT. Shadow IT refers tosituations where information technology systems and solutions are builtand used inside organizations without explicit organizational approval.The embodiments disclosed here can analyze events (e.g., containingmachine data) reflecting activity in an IT environment to identifyentities associated with the activity, information about the entities,and the logical structure (i.e., topology) of the IT environment inwhich they are operating. Once entities are profiled and the topology ofthe IT environment is discovered, this information can be modeled andused to detect anomalous communications activity within the ITenvironment. For example, FIG. 17 shows a high level representation 1702of many interconnected nodes (i.e., entities) communicating in anexample IT environment. Without further detail, this representation ofthe IT environment provides little to no insight into what types ofentities are active in the IT environment, what type of activity ispresent in the IT environment, or whether the activity is normal,anomalous, or even malicious. Using the techniques described herein, theactivity (as indicated in event machine data) can be analyzed to providea clearer picture 1704 of the types of entities active in theenvironment and where they are located in a logical arrangement (i.e., atopology) of the IT environment. Further, as indicated at detail 1706,this information can be used to detect anomalous activity, for example,in the form of communications indicative of a webshell attack in ademilitarized zone (DMZ) area of an IT environment.

FIG. 18 is a flow chart that describes at a high level an exampleprocess 1800 for implementing the techniques described herein.

In some embodiments, process 1800 begins at step 1802 with accessingdata associated with entity activity, for example in the form of events.

Next, this data is analyzed during an identity resolution stage at step1804 to resolve the identity for a particular entity. This can includeattributing a suitable identifier for the entity to at least some of theaccessed events.

Next, during a topology discovery stage at step 1806, entity activity isanalyzed to determine how entities associated with the IT environmentare logically located within the environment. For example, the logicalarrangement may be based on a layer of the Open Systems Interconnection(OSI) model. In some embodiments, the logical arrangement can bediscovered by analyzing the activity inferences can be made regardingwhether the entity is operating in a local area network (LAN), in a widearea network (WAN), in a DMZ, or external to the IT environment.

Next, at step 1808, during a behavioral profiling stage, the activity ofidentified entities is analyzed, and in some cases, the entities areassociated with certain classes of entities (e.g. server, laptop,printer, IOT device, etc.). In some embodiments, this step may includean additional step or sub step in which the activity is analyzed todetermine if a particular entity is behaving as a client or server withrespect to another entity. This information can be contained inbehavioral profiles generated for identified entities.

Next, at step 1810, information regarding the identified entities, theirbehavior, and the topology of the IT environment is captured andorganized in the form of a generated entity relationship graph. Forexample, in some cases the entity relationship graph may includemultiple nodes representing the identified entities. The graph mayfurther include edges connecting the nodes representing interactionbetween the entities. In some embodiments, the edges can includedirectionality (e.g., in the form of an arrow pointing to one of twonodes) that indicates the normal flow of activity between the two nodes.For example, an entity relationship graph may contain directional edgesindicating normal activity between client to server, server to client,server to server (DMZ to LAN), client to client (LAN to LAN), etc.

A generated entity relationship graph can, at step 1812, be monitoredfor changes to detect anomalies (step 1814). For example, in anembodiment, anomalies are detected when the directionality of an edgeconnecting two nodes in the graph shifts (e.g., reverses) indicating anabnormal flow of activity.

Note that process 1800 described above is an example provided forillustrative purposes. In some embodiments, other processes may havefewer or more steps than are shown in FIG. 18 and may reorder the stepsdifferently than as shown. For example, in some embodiments the steps ofidentity resolution, topology discovery and behavioral profiling may bepart of a single overall processing step that determines the types ofentities in a given IT environment and how they are related to eachother based at least in part on their activity.

FIG. 19 shows a flow diagram 1900 that illustrates how, in anembodiment, raw machine data reflecting activity in an IT environmentcan be processed. Examples of data include events that are accessed froma data intake and query system 108. The data can be processed usingmachine learning based processing engines, for example, implemented inan Apache Spark™ based data processing framework. Note that the Sparkdata processing framework illustrated in FIG. 19 is an example. In someembodiments, processing of events to performed certain techniques (e.g.,as described with respect to FIG. 18) can be performed in data intakeand query system 108 and/or the network security system 120. Forexample, in some embodiments, one or more of the process steps describedwith respect to FIG. 18 may be performed in any of a real timeprocessing path or batch processing patch of a network security system120 described in more detail with respect to FIGS. 11-14.

3.1 Identity Resolution

Identity resolution can be the first step in gaining insight intoentities in an IT environment, particularly in environments that includea dynamic host configuration protocol (DHCP) service (i.e., to providenon-static IP addresses). In such environments, a device operating on anetwork may be associated with multiple different IP addresses overtime. Naively attributing certain patterns of behavior to a particularIP address may lead to incorrect analysis. Accordingly, an identityresolution process can be employed to map identifiers (e.g., a MACaddress or an IP address) to a certain entity (e.g., a device) which canremain valid for a period of time. By mapping identifiers to aparticular entity, the identity of the particular entity can beattributed to certain events as they are accessed. For example, activityby a particular entity may be reflected, in a first event, as associatedwith a particular IP address and, in a second event, as associated witha particular MAC address. Without resolving the identity of theparticular entity, the two events may appear to be attributable todifferent entities. However, by mapping the particular IP address to theparticular MAC address, the activity reflected in both events can beattributed to the particular entity. Identity resolution mapping can bedynamically updated as the time goes by and more events are accessed. Asthe IT environment changes, an identity resolution module can derive newmapping arrangements. For example, the same IP address can becomeassociated with a different MAC address. Note that, for the particularcase of DHCP services, it is generally easier to estimate when aparticular version of a device resolution model should expire, because aDHCP service setting typically includes explicit lease expirationprovisions.

FIG. 20 shows an example workflow for entity identity resolution,according to some embodiments. As shown in FIG. 20, multiple statetables, for example a DHCP state table 2002, IP multimedia subsystem(IMS) state table 2004 and an active directory (AD) state table 2006 maybe maintained that include identifiers for a plurality of entities in agiven IT environment. In some embodiments these identifiers may belogged as certain entities (e.g. devices) connect to a given network.For example, a DHCP service database 2003 may include a list ofnon-static IP address assigned to connected clients that is dynamicallyupdated as the IP address leases are assigned and released. Similarly,asset identifiers may be pulled or received from an active directorydatabase 2007. In some cases identifiers may be filtered beforeplacement in any of the active state tables. For example, informationfrom AD database 2006 may be passed through an event ID filter 2009 sothat only relevant information is placed in the active AD state table2006. In some embodiments identifiers for entities active in an ITenvironment can be pulled from the raw data included in events accessed,for example from a data intake and query system 108.

As further shown in FIG. 20, the multiple identifier state tables fromdisparate sources are then aggregated into a main active ID state table2008 that may associate multiple identifiers (e.g., an IP address, a MACaddress, a domain name, etc.) with a single entity active in the ITenvironment. For example, FIG. 21 shows the contents of an example DHCPstate table 2002 and an example AD state stable 2006 that includesmultiple disparate identifiers for the same entities. As shown in FIG.22, the DHCP state table 2002 and an AD state stable 2006 can beaggregated into the active ID state table 2008, in some cases in realtime as events are received. Returning to FIG. 20, the active ID statetable 2008 can be further annotated with entity identifiers pulled orreceived from multiple other sources. For example, an identityannotator/normalization engine 2020 can annotate the active ID statetable 2008 with data (e.g., in the form of events or raw machine data)from firewall services 2030, proxy services 2032, and/or virtual privatenetwork (VPN) services 2034.

In the context of the network security system 120, a process foridentity resolution may be performed during a data intake andpreparation stage the identity resolution module 1412 described withrespect to FIG. 14. Specifically, after the entities are identified inthe tokens, the identity resolution module 1412 is operable to performan identity resolution process, which enables keeping track of whichidentifiers are associated with which entities across an IT environment.

In the context of computer security and especially unknown threatdetection, information about entity behavior can be very important.However, as previously discussed, not all events/activities/logs includecomprehensive entity information. Consider a typical firewall event asan example. Except for a few advanced firewall products, many typicalfirewalls may not know and do not record certain entity information.Accordingly, such firewall products are not able to accurately attributea particular user to an event. Therefore, many times even when aparticular communication is determined to be malicious, traditionalsecurity products are unable to attribute the malicious behavior to aparticular user. Embodiments disclosed here provide that, when logs ordevice-level events do not capture the user information, the identityresolution module 1412 in the data intake and preparation stage canattribute those events (and behaviors) to the right user. Similarly, theidentity resolution module 1412 can attribute events to the multipleidentifiers associated with a single entity (e.g. a device).

The identity resolution module 1412 can gain the knowledge by observingthe system environment (e.g., based on authentication logs), therebybuilding the intelligence to make an educated identity resolutiondetermination. That is to say, the identity resolution module 1412 isable to develop entity identity intelligence specific and relevant tothe system's environment without receiving explicit user identityinformation.

To facilitate this fact-based identity resolution functionality in asecurity system 120, the identity resolution module 1412 can utilize amachine learning model to generate and track a probability ofassociations between multiple identifiers. Specifically, after theidentifiers are extracted from an event (e.g., by the field mapper1408), the identity resolution module 1412 can identify whether theevent includes a user identifier or a machine identifier, and can createor update the probability of association accordingly. The modelinitiated by the identity resolution module 1412 can, in someembodiments, obtain the information it needs, e.g., obtaining machineidentifiers in an event, through one or more interfaces. A machineidentifier is an identifier that can be associated with a machine, adevice, or a computing system; for example, a machine identifier can bea media access control (MAC) address, or an Internet Protocol (IP)address. Different machine identifiers can be generated by the samemachine. A user identifier is an identifier that can be associated witha user; for example, a user identifier can be a user login identifier(ID), a username, or an electronic mail address. Although notillustrated in FIG. 14, some embodiments of the identity resolutionmodule 1412 can resolve a user identity of a particular user by, forexample, querying a database using a user identifier as a key. Thedatabase, which may be a human resource management system (HRMS), canhave records indicating a number of user identifiers that are registeredto the user identity. Note that, in some alternative embodiments, a useridentifier may be directly treated as a user for simpler implementation,even though such implementation may not be an ideal one becausebehaviors of the same user may not be detected because the user has useddifferent user identifiers.

More specifically, a machine learning model can have different phases,for example, a training phase (after initiation and before ready) and anactive phase (after ready and before expiration). In a training phase ofa machine learning model, if an event that is received involves both auser and a machine identifier (e.g., if the event data representing theevent has both a user identifier and a machine identifier), then machinelearning model that is employed by the identity resolution module 1412can use this event to create or update the probability of associationbetween the user and the machine identifier. For example, when anauthentication event is received (e.g., when a user logs into aparticular machine) and involves a user (e.g., identified by a useridentifier such as a username) and a machine identifier, the modellearns that the user is now associated with the machine identifier, atleast for a period of time until the user logs out or times out from theparticular machine.

As more events are received, the model can become increasingly bettertrained about the probability of association between the user and themachine identifiers. In some embodiments, the machine learning modelsused for identification resolution may be entity specific. In otherwords, each identified entity may be associated with a separate machinelearning based model for identity resolution. It is also noted that themachine learning models used in identity resolution are generallysimpler than those models that would be used for anomaly and threatdetection. In many embodiments, the models that are used in the identityresolution or device resolution are time-sequenced probabilistic graphs,in which the probability changes over time.

According to a number of embodiments, the models that are used togenerate and track the probability of association between each user andpossible machine identifiers are time-dependent, meaning that a resultfrom the models has a time-based dependence on current and past inputs.The time dependence is useful to capture the scenario where a device isfirst assigned or given to a particular user, and is subsequentlyreassigned to a different user, which happens often in a largeorganization. To achieve this, in some embodiments, the identityresolution module 1412 can initiate, for a given user, differentversions of the machine learning model at different point of time, andeach version may have a valid life time. As events related to the givenuser arrive, versions of a machine learning model are initiated,trained, activated, (optionally) continually updated, and finallyexpired.

The models can be trained and, in some implementations, continuallyupdated after their activation, by relevant events when the events arereceived. An example of a relevant event is an authentication event,which inherently involves a user (e.g., which may be represented by auser identifier) and a number of machine identifiers (e.g., an IPaddress or a MAC address). Depending on the model, other criteria for anevent to be considered relevant for model training or updating purposesmay include, for example, when a new event includes a particular machineidentifier, a particular user identifier, or the recency of the newevent. Moreover, some models may assign a different weight to the newevent based on what type of event it is. For example, given that the newevent is an authentication event, some models assign more weight to aphysical login type of authentication event than to any other type ofauthentication event (e.g., a remote login).

Depending on the particular deployment, the machine learning model canbe considered trained and ready when one or more criteria are met. Inone example, a version of the model can be considered trained when acertain number of events have gone through that version of the model. Inanother example, a version of the model can be considered trained when acertain time period has passed after the version of the model isinitiated. Additionally or alternatively, a version of the model isconsidered trained when a certain number of criteria are met (e.g., whenthe model becomes sufficiently similar to another model). Additionaldetails of machine learning models that can be employed (includingtraining, readiness, activation, and expiration) by various engines andcomponents in the security platform are discussed in other sections ofthis disclosure.

After a version of a model is sufficiently trained (e.g., when theprobability of association exceeds a confidence threshold, which dependson the model's definition and can be tuned by the administrator for theenvironment), the identity resolution module 1412 then can activate theversion of the model. Thereafter, when a new event arrives, if the newevent meets certain criteria for the identity resolution, the identityresolution module 1412 can create a user association record (e.g., inmemory) indicative that the new event is associated with a particularuser. The criteria for the identity resolution can include, for example,when the new event includes a machine identifier (regardless of whetherit also includes a user identifier), or when the new event is receivedduring a time period which the version is active. It is observed thatthe identity resolution technique is especially useful to help identifyan event that includes only a machine identifier but no user identifier.

Based on this user association record, the identity resolution module1412 can annotate the new event to explicitly connect the new event tothe particular user. For example, the identity resolution module 1412can add, as a field, the particular user's name to the new event in itsassociated event data. Alternatively, the identity resolution module1412 can annotate the new event by adding a user identifier that belongsto the particular user. In addition, the identity resolution module 1412can send the user association record to a cache server that isimplemented based on Redis™.

With the fact-based identity resolution techniques disclosed herein, asecurity system has the ability to attribute an event that happens on adevice to a user, and to detect behavioral anomalies and threats basedon that attribution. The security system can achieve this without theneed of maintaining an explicit look-up file and irrespective of whatthe data source is (i.e., regardless of whether a data source for anevent includes a user identifier or not).

Although not illustrated in FIG. 14, an embodiment of the data intakeand preparation stage can also implement a device resolution module tocreate an association between one machine identifier and another. In amanner similar to how the identity resolution module 1412 tracks thepossibility of association between a user and a machine identifier, thedevice resolution module can track the possibility of associationbetween a first machine identifier and a second machine identifier.Thereafter, when a new event is received, if the event includes thefirst machine identifier but not the second, the device resolutionmodule can create a machine association record indicative that the newevent having the first machine identifier is associated with the secondmachine identifier. Optionally, the machine identifier can be translatedinto a more user-friendly machine name, such as “Tony's Laptop.”

3.2 Topology Discovery

During a topology discovery stage, activity is analyzed to determine howentities associated with the IT environment are logically located withinthe environment. For example, the logical arrangement may be based on alayer of the Open Systems Interconnection (OSI) model. In such anembodiment, inferences can be made based on activity about whether anidentified entity is operating, for example, within a local area network(LAN), wide area network (WAN), a demilitarized zone (DMZ), or externalto the IT environment. In other words, discovering the topology of an ITenvironment may include inferring the logical relationships between theentities interacting in the IT environment based on their activity.

Entities operating in an IT environment generally should behave incertain patterns depending on their type and their location within thetopology. For example, the DMZ in an IT environment essentially refersto the edge of the LAN at the firewall. If a particular IT environmentincludes a web server operating in the DMZ, a risk of attack (e.g.,webshell attack) may exist. Accordingly, it is important to monitor foranomalous communications to an or from such a web server to effectivelydetect such an attack. This requires knowing that entity activityindicated in accessed events is associated with such a web server andthat the web server is operating in the DMZ, so to speak.

As mentioned, monitoring the behavior of certain entities can uncoverinsight into their location with a topology of an IT environment. Takefor example an identifier such as an IP address. In some embodiments, atopology discovery stage process may include tracking source/destinationbehavior for a given IP address. For example, how many hosts talk to theentity associated with the IP address (In degree) and how may hosts aretalked to by the entity associated with the IP address (out degree).Similarly, the frequency of serving as a destination entity and thefrequency of serving as a source entity can be tracked for differenttime periods to reveal the normal state of operation for the entity. Insome cases, IP subnet behavior can be tracked for certain entities(e.g., the number of LAN to LAN interactions and/or the number of LAN toWAN interactions).

Once the entity is identified as being located in a particular topologyzone (e.g. the DMZ), the next step is to determine what type of entityit is. As mentioned an entity may refer to an number of differentcategories of things including devices, users, services, applications,etc. However, even within a particular type category (e.g., devices),entities can be associated with a number of commonly occurring classesincluding, but not limited to, desktop computers, laptop computers,smart phones, tablets, servers (MS, *nix, web, printer, etc.), printers,and IOT devices (e.g., biomedical devices, energy meters, etc.).

In some embodiments, the classes of assets in a given IT environment maybe predefined or user defined based on known behavioral characteristics.For example, a server may be distinguishable from a client device basedon communications flow characteristics (e.g., byte distribution ratios).Similarly, devices may be distinguishable based on application layercharacteristics. For example, server may be associated with netbiosupdates and may interact with a number of unique domains every day.Conversely, a client-side device may exhibit non-uniform activitypatterns through applications such as Facebook, Twitter, etc.

In some embodiments, machine-learning may be applied to data included inevents to determine a set of classes of entities active in an ITenvironment. For example, machine-learning based clustering algorithms,such as k-means, can be applied to a set of entities to classify theminto a certain number of clusters. The number of clusters will depend onhow the clustering algorithms are applied; however, this process can bemade dynamic to arrive at a stable number of classifications that areeffective in a given context.

Following the topology discovery stage, you are left with a set oftopology labels (e.g., LAN, WAN, DMZ, or external) and entity labels(e.g., desktop computers, laptop computers, smart phones, tablets,servers (MS, *nix, web, printer, etc.), printers, and IOT devices (e.g.,biomedical devices, energy meters, etc.). In some embodiments, theselists of labels are automatically generated during the topologydiscovery stage, but can also be made user configurable to allow foradding of different topology labels and/or entity labels.

In some embodiments, an appropriate topology and/or entity label may beapplied to an identified entity, for example, during a behavioralprofiling stage described below. In other words, a topology label and/orentity class label may be tied to a suitable entity identifierdetermined during the identity resolution stage.

As would be expected, IT environments do not typically remain static. Asentities connect and disconnect, and organizations and architecturesshift, the topology of the environment similarly changes. Accordingly,in some embodiments, the topology (including the topology labels andentity labels) is updated as additional events are received andprocessed.

As previously discussed, the end goal of entity fingerprinting may insome cases be to detect anomalous activity. However, informationassociated with an environment topology may in itself be useful. Forexample, information about the entities in an IT environment and theirlogical location within the environment would be useful to an admin usermanaging the IT environment. Accordingly, in some embodiments,information associated with the topology may be output to a user, forexample, via a graphical user interface. For example, a graphicalrepresentation of an entity relationship graph similar to as shown inFIG. 17 may be output to a user via a graphical user interface. In someembodiments the output may be interactive allowing the user to explorethe topology of the IT environment.

3.3 Behavioral Profiling

During a behavioral profiling stage, the activity of identified entitiesis analyzed and in some cases the entities are associated with certainclasses of entities (e.g., server, laptop, printer, IOT device, etc.).As previously mentioned, these entity classes may have been determinedduring a topology discovery stage described above.

An entity behavioral profile may include a vector having a plurality ofvalues indicative of characteristics for certain features. For example,FIG. 23 shows an example behavioral profile 2300 in the form of a vectorfor a device identified by a particular IP address, “10.138.32.32.” Thevalues included in a behavioral profile for a particular entity may bebased on an analysis of raw machine data indicative of activity by theentity over a period of time. For example, as shown in FIG. 23, valuesincluded in a behavioral profile may include information associated withany of traffic load, applications used as a client, applicationsprovided as a server, port uses, zones belonged to, zones talked to,bytes sent, session duration, etc.

The behavioral profile of a tracked entity (e.g., a device) can be usedto determine what type of entity classification the tracked entity fallsunder. Recall that the classes of entities occurring in a particular ITenvironment may be predefined, user defined, or in some cases discoveredusing machine learning, for example, during a topology discovery stage.In any case, the behavioral profile for a particular entity can becompared to a representative profile for a group of entities associatedwith a particular entity class to determine if a match exits. Forexample, in some embodiments the behavioral profile may be representedin the form of a histogram that charts baseline values for a particularentity for multiple dimensions (e.g., any of those described withrespect to FIG. 23). This histogram for the particular entity can thenbe compared to a histogram charting baseline values for similardimensions for groups of entities previously associated with aparticular class of entities.

As a result of the behavioral profile stage, an identified entity (e.g.,as associated with a particular identifier) can be associated with oneor more of a plurality of classes of entities. For example, aspreviously mentioned, classes may include desktop computers, laptopcomputers, smart phones, tablets, servers (e.g., MS, *nix, web, printer,etc.), printers, and IOT devices (e.g., biomedical devices, energymeters, etc.). Once the classification of an entity is determined, anentity label may be applied to the entity's behavioral profile toindicate that the entity is associated with a certain class.

Additional Examples of functionality related to behavioral profiling aredescribed in U.S. patent application Ser. No. 15/418,464, entitled“SECURITY MONITORING OF NETWORK CONNECTIONS USING METRICS DATA”, filedon 27 Jan. 2017, and which is hereby incorporated by reference in itsentirety for all purposes.

3.4 Client/Server Relationship Discovery

As described above, by analyzing activity in an IT environment (e.g. byprocessing received events), a system in accordance with the presentteachings can identify entities active in the IT environment, candiscover how these identified entities are located within a topology ofthe IT environment, and can determine what the identified entities are.Using this information an entity relationship graph can be generatedthat represents the many identified entities in an IT environment andtheir relationships (e.g., based on interaction).

FIGS. 24 and 25 describe an example entity relationship discovery andrecordation technique, which can be implemented in the data intake andpreparation stage described with respect to FIG. 14. To facilitatedescription, FIGS. 24 and 25 are explained below with reference to FIG.14. The entity relationship discovery and recordation technique can beperformed by, for example, the relationship graph generator 1410.Specifically, after the entities are identified, the relationship graphgenerator 1410 is operable to identify a number of relationships betweenthe entities, and to explicitly record these relationships between theentities, for example, in the form of entity relationship graphs. Someimplementations of the relationship graph generator 1410 generate asingle relationship graph for each event; such an event-specificrelationship graph may also be called a “mini-graph.” In someembodiments the graph generator 1410 is operable to generate an overallcomposite graph (e.g., composed of multiple mini-graphs) reflecting theentity relationships based on a plurality of accessed events. Further,some implementations incorporate the generated relationship graphs intothe data of events associated with the relationships, in the form of adata structure representing the relationship graph. A graph in thecontext of this description includes a number of nodes and edges. Eachnode in the relationship graph represents one of the entities involvedin the event, and each edge represents a relationship between two of theentities. In general, any event involves at least two entities with somerelationship between them (e.g., a device and a user who accesses thedevice) and therefore can be represented as an event-specificrelationship graph.

The graph generator 1410 can identify a relationship between entitiesinvolved in an event based on the actions that are performed by oneentity with respect to another entity. For example, the graph generator1410 can identify a relationship based on comparing the action with atable of identifiable relationships. Such a table of identifiablerelationship may be customizable and provides the flexibility to theadministrator to tailor the system to his data sources (describedabove). Possible relationships can include, for example, “connects to,”“uses,” “runs on,” “visits,” “uploads,” “downloads,” “successfully logsonto,” “restarts,” “shuts down,” “unsuccessfully attempts to log onto,”“attacks,” and “infects.” Also, the identified relationship between theentities can be indicative of the action, meaning that the identifiablerelationship can include the action and also any suitable inference thatcan be made from the action. For example, an event that records a GETcommand (which is an action) may indicate that the user is using amachine with a certain IP address to visit a certain website, which hasanother IP address. In practice, however, the number of identifiablerelationships can be directly correlated to the size of the graph, whichmay impact the security platform's responsiveness and performance. Also,identifiable relationships can include a relationship between entitiesof the same type (e.g., two users) or entities of different types (e.g.,user and device).

In some embodiments, specific details on how to construct the edges andthe identifiable relationships are recorded in a configuration file(e.g., snippet). For example, a portion of the configuration file canspecify, for the relationship graph generator 1410, that an edge is tobe created from an entity “srcUser” to another entity “sourceIP,” with arelationship that corresponds to an event category to which the eventbelongs, such as “uses.”

FIG. 24 illustrates raw machine data 2400 (e.g. as included in an event)received by the data intake and preparation stage. The raw machine data2400, representing occurring activity may be part of log data generatedby a web gateway server. The web gateway is located where networktraffic in and out the environment goes through, and therefore can logthe data transfer and web communication from a system inside theenvironment. The particular event as represented by the event data 2400indicates that, at a particular point of time identified by thetimestamp, the user “psibbal” uses the IP address “10.33.240.240” tocommunicate with an external IP address “74.125.239.107,” and transfers106 bytes of data. The status code of that event is “200,” and the eventis a TCP event where the HTTP status is “GET.” As illustrated, the data2400 also includes a significant amount of additional information.

Using the aforementioned techniques (e.g., the parsers 1406, and thefield mapper 1408), the graph generator 1410 can readily identify thatthe event represented in the FIG. 24 involves a number of entities, suchas the user “psibbal,” the source IP “10.33.240.240,” the destination IP“74.125.239.107,” and an URL “sample.site.com.” The graph generator 1410also identifies that an action “GET” is involved in the event.Accordingly, the graph generator 1410 can compare the action to thetable of identifiable actions, identify one or more relationshipsbetween the entities, and create an event-specific relationship graph2500 based on the event. As shown in FIG. 25, the relationship graph2500 includes the entities that are involved in the events. Each entityis represented by a different node. The relationship graph 2500 alsoincludes edges that link the nodes representing entities. The identifiedrelationships between the entities are the edges in the graph 2500. Therelationship graph 2500 can be stored in known data structures (e.g., anarray) suitable for representing graphs that have nodes and edges.

Note, however, that the components introduced here (e.g., the graphgenerator 1410) may be tailored or customized to the environment inwhich the platform is deployed. As described above, if the networkadministrator wishes to receive data in a new data format, he can editthe configuration file to create rules (e.g., in the form of functionsor macros) for the particular data format including, for example,identifying how to tokenize the data, identifying which data are theentities in the particular format, and/or identifying the logic on howto establish a relationship. The data input and preparation stage thencan automatically adjust to understand the new data format, identifyidentities and relationships in event data in the new format, and createevent relationship graphs therefrom.

Then, in some embodiments, the graph generator 1410 attaches therelationship graph 2500 to associated events. For example, the graph2500 may be recorded as an additional field of the in an associatedevent. In alternative embodiments, the relationship graph 2500 can bestored and/or transferred individually (i.e., separate from events) tosubsequent nodes in the security platform. After additional processes(e.g., identity resolution, sessionization, and/or other decorations) inthe data intake and preparation stage, the relationship graph 2500 canbe sent to a distributed messaging system, which may be implementedbased on Apache Kafka™.

A messaging system (e.g., Apache Kafka™) can also accumulate oraggregate, over a predetermined period of time (e.g., one day), multiplerelationship graphs that are generated from the events as the events areaccessed. As such, at the messaging system, the relationship graphs(mini-graphs) for all events, or at least for multiple events, can becombined into a larger, composite relationship graph. For example, acomputer program or a server can be coupled to the messaging system toperform this process of combining individual relationship graphs into acomposite relationship graph, which can also be called an enterprisesecurity graph. The composite relationship graph can be stored, forexample, as multiple files, one file for each of multiple predeterminedtime periods. The time period depends on the environment (e.g., thenetwork traffic) and the administrator. In some implementations, thecomposite relationship graph is stored (or “mined” in data miningcontext) per day; however, the graph mining time period can be a week, amonth, and so forth.

In some embodiments, event-specific relationship graphs are merged intothe composite relationship graph on an ongoing basis, such that thecomposite relationship graph continuously grows over time. However, insuch embodiments it may also be desirable to remove (“age out”) datadeemed to be too old, from the composite relationship graph,periodically or from time to time.

In some embodiments, the nodes and edges of the composite graph arewritten to time namespaces partitioned graph files. Then, each smallersegment can be merged with a master partition (e.g., per day). The mergecan combine similar nodes and edges into the same record, and in someembodiments, can increase the weight of the merged entity nodes. Notethat the exact order of the events' arrival becomes less important,because even if the events arrive in an order that is not the same ashow they actually took place, as long as the events have timestamps,they can be partitioned into the correct bucket and merged with thecorrect master partition. Some implementations provide that thecomposite graphs can be created on multiple nodes in a parallelizedfashion.

In this manner, this composite relationship graph can include allidentified relationships among all identified entities involved in theevents that take place over the predetermined period of time. As thenumber of events received by the security platform increases, so doesthe size of this composite relationship graph. Therefore, even though arelation graph from a single event may not carry much meaning from asecurity detection and decision standpoint, when there are enough eventsand all the relationship graphs from those events are combined into acomposite relationship graph, the composite relationship graph canprovide a good indication of the behavior of many entities, and thequality/accuracy of this indication increases over time as the compositerelationship graph grows. Then, the subsequent processing stages (e.g.,the complex processing engine) can use models to perform analytics onthe composite relationship graph or on any particular portion (i.e.,“projection”, discussed further below) of the composite relationshipgraph. In some embodiments, the composite relationship graph ispersistently stored using a distributed file system such as HDFS™.

In some embodiments, when various individual events' relationship graphs(along with their associated decorated events) are stored in themessaging system but have not yet been combined to create the compositerelationship graph, each such event's relationship graph can be furtherupdated with any information (e.g., anomalies) that is discovered bydownstream processes in the security platform. For example, if an eventis found to be an anomalous, then the relationship graph associated withthat anomalous event can be updated to include this information. In oneexample, the individual relationship graph of that anomalous event isrevised to include an anomaly node (along appropriate edges), so thatwhen the composite relationship graph is created, it can be used todetermine what other entities might be involved or affected by thisanomaly.

At least in some embodiments, the composite graph enables securitysystem 120 to perform analytics on entity behaviors, which can be asequence of activities, a certain volume of activities, or can be customdefined by the administrator (e.g., through a machine learning model).By having an explicit recordation of relationships among the events, therelationship graph generator 1410 can enable the analytics engines toemploy various machine learning models, which may focus on differentportions or aspects of the discovered relationships between all theevents in the environment, in order to detect anomalies or threats.

FIG. 26 shows an example representation of an entity relationship graph2600 similar to the graph 2500 shown in FIG. 25. Note that the examplegraph 2500 shown in FIG. 25 is event-specific and reflects the activityindicated by the machine data in event 2400 shown in FIG. 24.Conversely, the entity relationship graph 2600 shown in FIG. 26 mayrepresent a comprehensive graph that takes into account relationshipsbetween the entities as well as the type of entity and the location ofthe entity within the topology of an IT environment. As mentioned, insome embodiments a graph similar to graph 2600 may be composed ofmultiple event-specific mini-graphs. As with graph 2500, entityrelationship graph 2600 includes a plurality of nodes and edgesconnecting the plurality of nodes. In the example graph 2600, theplurality of nodes represent identified entities in an IT environment.For example, graph 2600 includes a node 2610 representing a computerexternal to the IT environment, a node 2608 representing a web server inthe DMZ, a node 2606 representing a server in the LAN, and nodes 2602and 2604 representing computers in the LAN. Again, information regardingthe entities (e.g. identity, type, location within topology) associatedwith the depicted nodes may have been gathered or inferred during thepreviously described stages. Note that the example representation of anentity relationship graph 2600 shown in FIG. 26 is simplified forclarity and is not necessarily indicative of the structure of such agraph in practice.

The edges connecting the nodes in the graph represented in FIG. 26indicate some level of interaction (e.g. in the form of transmitteddata, messages, etc.) between entities, but not necessarily whether theinteraction is of interest or concern. Accordingly, in some embodiments,during a client/server relationship discovery stage, the interactionsbetween entities can be analyzed (e.g. by processing events) to baselinecertain interactions, particularly those that indicate client serverrelationships. Establishing baselines for entity interactions can insome cases rely on analysis of the bytes flowing between the entities.For example, by analyzing communications between entities (e.g. totalbytes transferred between devices, the ratio of bytes transferred, timeof the transfers, average packet sizes for the transfers, the rate atwhich data is transferred, etc.) a system in accordance with the presentteaching may establish a baseline direction of communication that isperhaps indicative of a client server relationship between entities.Note that this analysis may depend on the previously determinedclassification associated with the entity as well. In other words,analysis of interactions may be weighted based on the previouslydetermine entity types in establishing interaction baselines.

In some embodiments an interaction baseline between entities can berepresented in an entity relationship graph by adding directionality tothe edge connecting the entities. For example, FIG. 27 shows arepresentation of an entity relationship graph 2700 similar to graph2600 depicted in FIG. 26 except that the edges connecting the nodes arenow represented as arrows indicating a baseline directionality incommunication between nodes, for example, based on a client-serverrelationship analysis. Note that directionality of the edges depicted inFIG. 27 are exemplary and provided for illustrative purposes. Baselinedirectionality of edges connecting nodes in a graph representative ofanother IT environment will differ based on the particular activityassociated with that environment.

3.5 Monitoring an Entity Relationship Graph

Certain types of malicious attacks can present themselves in the form ofrare communications between certain entities. For example, a webshell isa specific type of attack that is usually tied to a concerted campaignin which a malicious actor operating outside of an IT environmentestablishes a backdoor at a vulnerable device operating in the DMZ(e.g., a web server) instead of directly trying to attack LAN devicesprotected behind a firewall. In such a webshell attack, the maliciousactor may inject a lightweight PHP script into a vulnerable web servicein the DMZ. This script effectively establishes a beach head for themalicious actor, thereby allowing them to move laterally into the LAN toattack systems or access sensitive information.

The previously described entity relationship graph can be monitored todetect such anomalous activity. As previously described with respect toFIG. 27, an entity relationship graph may include nodes representingentities with edges connecting the nodes representing relationshipbetween the entities. The edges can include directionality in the formof arrows representing baseline interaction between the entities.Accordingly, the graph can be monitored to detect interactions that donot conform with certain established baselines and are thereforeindicative of anomalous activity. For example, in some embodiments thisprocess may include monitoring communications (e.g., by processingevents) for specific instance of interactions that do not conform withestablished baselines.

In some embodiments this may include monitoring an entity relationshipgraph for changes in established baselines, for example a flip indirectionality of an edge connecting multiple nodes. In someembodiments, monitoring of the graph may focus particularly are certainsegments and entity types, for example web servers operating in the DMZ.In any case, an entity relationship graph can be monitored to detectanomalies on certain rare paths including, but not limited to, client toserver anomalies, server to client anomalies, client to client (LAN toLAN) anomalies, and server to server (DMZ to LAN) anomalies.

In some embodiments, once detected, indications of these anomalies canbe output to a user via user interface. For example, indications ofdetected anomalies can be output as notable events via an interfacesimilar to as depicted with respect to FIGS. 15A-15B. In someembodiments detected anomalies can be processed using machine learningbased models in a real time or batch processing pipeline along withother detected anomalies to identify threat indicators and possiblyconfirm active threats to the security of the IT environment.

What is claimed is:
 1. A computer implemented method comprising:accessing a set of events associated with activity by a plurality ofentities in an information technology (IT) environment, wherein eachevent in the set of events includes a portion of raw machine data thatreflects activity in the IT environment and that is produced by acomponent of the IT environment, wherein each event is associated with atimestamp extracted from the raw machine data; determining a topology ofthe IT environment by processing at least some of the accessed set ofevents; generating an entity relationship graph based on the topology ofthe IT environment; wherein the entity relationship graph includes: aplurality of nodes representative of the plurality of entities in the ITenvironment; and edges connecting the plurality of nodes, the edgesrepresenting relationships and activity between entities represented bythe plurality of nodes; wherein each edge includes a directionality thatindicates a normal flow of communication between the entitiesrepresented by the nodes connected to the edge; and monitoring theentity relationship graph to detect an anomaly.
 2. The method of claim1, wherein the anomaly is detected in response to detecting a change inthe entity relationship graph.
 3. The method of claim 1, wherein theanomaly is detected in response to detecting a shift in thedirectionality of an edge in the entity relationship graph.
 4. Themethod of claim 1, wherein the anomaly is indicative of anomalouscommunication between a particular entity of the plurality of entitiesand the at least one other entity of the plurality of entities.
 5. Themethod of claim 1, wherein the anomaly is indicative of a web shellattack.
 6. The method of claim 1, wherein monitoring the entityrelationship graph includes: focusing monitoring on a portion of theentity relationship graph associated with a particular logical locationin the topology of the IT environment.
 7. The method of claim 1, furthercomprising: outputting, via a user interface, an indication of thedetected anomaly to a user.
 8. The method of claim 1, wherein theanomaly is detected based on detecting that the directionality haschanged in at least one edge.
 9. The method of claim 1, wherein theanomaly is detected in response to identifying a communication betweenentities that does not conform with a directionality of an edgeconnecting nodes associated with the entities.
 10. The method of claim1, further comprising: updating the entity relationship graph asadditional events are accessed and processed.
 11. The method of claim 1,further comprising: associating an identifier to a particular entity ofthe plurality of the plurality of entities, the identifier extractedfrom at least some of the set events; wherein the identifier includesany one or more of: a domain name, a uniform resource locater (URL),uniform resource identifier (URI), a unique identifier (UID), anInternet Protocol (IP) address, a Media Access Control (MAC) address, adevice identification, or a user identification.
 12. The method of claim1, further comprising: extracting a plurality of identifiers from atleast some of the accessed set of events; and associating the pluralityof identifiers to a particular entity of the plurality of entities. 13.The method of claim 1, further comprising: updating an identityresolution state table in real time as the set of events are accessed,the identity resolution state table associating a plurality ofidentifiers to a particular entity of the plurality of entities, theplurality of identifiers extracted from at least some of the accessedset of events.
 14. The method of claim 1, wherein determining thetopology of the IT environment by processing at least some of theaccessed set of events includes: inferring logical relationships betweenthe plurality of entities based on the activity by the plurality ofentities.
 15. The method of claim 1, wherein determining the topology ofthe IT environment by processing at least some of the accessed set ofevents includes: determining a plurality entity classes based on theactivity by the plurality of entities.
 16. The method of claim 1,wherein determining the topology of the IT environment by processing atleast some of the accessed set of events includes: inferring a logicallocation of a particular entity of the plurality of entities in the ITenvironment based on activity by the particular entity; wherein thelogical location of the particular entity is any one of the logicallocations from a set of logical locations including: local area network(LAN); demilitarized zone (DMZ); wide area network (WAN); or external.17. The method of claim 1, wherein determining the topology of the ITenvironment by processing at least some of the accessed set of eventsincludes: applying a topology label to an identifier referencing aparticular entity of the plurality of entities, the topology labelindicative of the location of the particular entity in the ITenvironment; wherein the logical location of the particular entity isany one of: local area network (LAN); demilitarized zone (DMZ); widearea network (WAN); or external.
 18. The method of claim 1, furthercomprising: receiving a user input defining a location of a particularentity in the IT environment; applying a topology label to an identifierreferencing the particular entity based on the user input; and updatingthe topology of the IT environment based on the topology label.
 19. Themethod of claim 1, further comprising: updating the topology of the ITenvironment as additional events are accessed and processed.
 20. Themethod of claim 1, further comprising: outputting, via a user interface,information associated with the topology of the IT environment to auser.
 21. The method of claim 1, further comprising: associating aparticular entity of the plurality of entities with one of a pluralityof entity classes wherein the plurality of entity classes arepredefined, user-defined, or defined based on processing of at leastsome of the events using supervised and/or unsupervised machine learningclassification models.
 22. The method of claim 1, wherein the entityrelationship graph is further based on behavioral profiles for one ormore of the plurality of entities.
 23. The method of claim 1, furthercomprising: generating a histogram based on activity by a particularentity of the plurality of entities; comparing the histogram based onactivity by the particular entity with a histogram based on activity bya plurality of entities associated with a particular class of entity;and associating the particular entity with the particular class ofentities if, based on the comparison, a matching criterion is satisfied.24. The method of claim 1, further comprising: determining if aparticular entity of the plurality of entities is operating as a clientor a server relative to at least one other entity of the plurality ofentities.
 25. The method of claim 1, wherein the set of events areaccessed from a field-searchable data store, wherein a field is definedby an extraction rule or regular expression for extracting a subportionof text from the portion of raw machine data in an event to produce avalue for the field for that event.
 26. The method of claim 1, whereinthe plurality of entities include any of: a device; an application; auser; or data.
 27. The method of claim 1, wherein the events arereceived from a plurality of sources via an extract, transform, and load(ETL) pipeline.
 28. The method of claim 1, wherein the anomaly isdetected in real time as events are accessed.
 29. A computer systemcomprising: a processing unit; and a storage device having instructionsstored thereon, which when executed by the processor cause the computersystem to: access a set of events associated with activity by aplurality of entities in an information technology (IT) environment,wherein each event in the set of events includes a portion of rawmachine data that reflects activity in the IT environment and that isproduced by a component of the IT environment, wherein each event isassociated with a timestamp extracted from the raw machine data;determine a topology of the IT environment by processing at least someof the accessed set of events; generate an entity relationship graphbased on the topology of the IT environment; wherein the entityrelationship graph includes: a plurality of nodes representative of theplurality of entities in the IT environment; and edges connecting theplurality of nodes, the edges representing relationships and activitybetween entities represented by the plurality of nodes; wherein eachedge includes a directionality that indicates a normal flow ofcommunication between the entities represented by the nodes connected tothe edge; and monitor the entity relationship graph to detect ananomaly.
 30. A non-transitory computer-readable medium containinginstructions, execution of which in a computer system causes thecomputer system to: access a set of events associated with activity by aplurality of entities in an information technology (IT) environment,wherein each event in the set of events includes a portion of rawmachine data that reflects activity in the IT environment and that isproduced by a component of the IT environment, wherein each event isassociated with a timestamp extracted from the raw machine data;determine a topology of the IT environment by processing at least someof the accessed set of events; generate an entity relationship graphbased on the topology of the IT environment; wherein the entityrelationship graph includes: a plurality of nodes representative of theplurality of entities in the IT environment; and edges connecting theplurality of nodes, the edges representing relationships and activitybetween entities represented by the plurality of nodes; wherein eachedge includes a directionality that indicates a normal flow ofcommunication between the entities represented by the nodes connected tothe edge; and monitor the entity relationship graph to detect ananomaly.